Car Sharing and Relocation Strategies: a Case Study Comparison in the Italian Market

The sharing economy represents an economic model based on the sharing of goods and services. In particular, this paper examines car sharing model, an attractive alternative to a self-owned car which has found large interest in the recent literature in different research fields. This study aims to investigate innovative and effective relocation strategies based on the analysis of data on users’ consumptions, for the constantly growing car sharing system. For this purpose, after a literature review, the paper presents a case study focused on the car repositioning algorithm developed by one of the market leader in this sector: car2go. More in detail, the paper evaluates differences and similarities in the strategic management of this model within the Italian context, through a comparison among the cities of Rome and Milan. Empirical results and practical implications for users will be provided, by highlighting opportunities and threats concerning the different settings

Business Process Simulation: A Systematic Literature Review

Business process simulation marks an essential Business Process Management technique for analysing business processes and for reasoning about process improvement. Despite its importance, literature is lacking a comprehensive, updated overview of research contributions to the field of business process simulation. In this systematic literature review, we assess the present state of research on business process simulation including prior work between 1990 and 2016. Results reported in the present study assist in advancing the discussion on future research on business process simulation by compiling and analysing prior work. The present literature review focuses on prior research involving conceptual business process models, e.g., BPMN models, with a graphical model representation as a starting point for business process simulation and excludes other foundations to build simulation models

Repairing Alignments of Process Models

Process mining represents a collection of data driven techniques that support the analysis, understanding and improvement of business processes. A core branch of process mining is conformance checking, i.e., assessing to what extent a business process model conforms to observed business process execution data. Alignments are the de facto standard instrument to compute such conformance statistics. However, computing alignments is a combinatorial problem and hence extremely costly. At the same time, many process models share a similar structure and/or a great deal of behavior. For collections of such models, computing alignments from scratch is inefficient, since large parts of the alignments are likely to be the same. This paper presents a technique that exploits process model similarity and repairs existing alignments by updating those parts that do not fit a given process model. The technique effectively reduces the size of the combinatorial alignment problem, and hence decreases computation time significantly. Moreover, the potential loss of optimality is limited and stays within acceptable bounds

Filter techniques for region-based process discovery

The goal of process discovery is to learn a process model based on example behavior recorded in an event log. Region-based process discovery techniques are able to uncover complex process structures (e.g., milestones) and, at the same time, provide formal guarantees w.r.t. the model discovered. For example, it is possible to ensure that the discovered model is able to replay the event log and that there are bounds on the amount of additional behavior allowed by the model that is not present in the event log. Unfortunately, region-based discovery techniques cannot handle exceptional behavior. The presence of a few exceptional traces may result in an incomprehensible model concealing the dominant behavior observed. Hence, despite their promise, region-based approaches cannot be applied in everyday process mining practice. This paper addresses the problem by proposing two filtering techniques tailored towards ILP-based process discovery (an approach based on integer linear programming and language-based region theory). Both techniques help to produce models that are less over-fitting w.r.t. the event log and have been implemented in ProM. One of the techniques is also feasible in real-life settings as it, in most cases, reduces computation time compared to conventional region-based techniques. Additionally the technique is able to produce understandable process models that better capture the dominant behavior present in the event log. Keywords: Process mining, process discovery, integer linear programming, filterin

GIS in Healthcare

The landscape of healthcare is dynamic, gradually becoming more complicated with factors beyond simple supply and demand. Similar to the diversity of social, political and economic contexts, the practical utilization of healthcare resources also varies around the world. However, the spatial components of these contexts, along with aspects of supply and demand, can reveal a common theme among these factors. This book presents advancements in GIS applications that reveal the complexity of and solutions for a dynamic healthcare landscape

Modeling Lung Carcinoids with Zebrafish Tumor Xenograft

Lung carcinoids are neuroendocrine tumors that comprise well-differentiated typical (TCs) and atypical carcinoids (ACs). Preclinical models are indispensable for cancer drug screening since current therapies for advanced carcinoids are not curative. We aimed to develop a novel in vivo model of lung carcinoids based on the xenograft of lung TC (NCI-H835, UMC-11, and NCI-H727) and AC (NCI-H720) cell lines and patient-derived cell cultures in Tg(fli1a:EGFP)(y1) zebrafish embryos. We exploited this platform to test the anti-tumor activity of sulfatinib. The tumorigenic potential of TC and AC implanted cells was evaluated by the quantification of tumor-induced angiogenesis and tumor cell migration as early as 24 h post-injection (hpi). The characterization of tumor-induced angiogenesis was performed in vivo and in real time, coupling the tumor xenograft with selective plane illumination microscopy on implanted zebrafish embryos. TC-implanted cells displayed a higher pro-angiogenic potential compared to AC cells, which inversely showed a relevant migratory behavior within 48 hpi. Sulfatinib inhibited tumor-induced angiogenesis, without affecting tumor cell spread in both TC and AC implanted embryos. In conclusion, zebrafish embryos implanted with TC and AC cells faithfully recapitulate the tumor behavior of human lung carcinoids and appear to be a promising platform for drug screening

Automated Process Discovery: A Literature Review and a Comparative Evaluation with Domain Experts

Äriprotsesside kaeve meetodi võimaldavad analüütikul kasutada logisid saamaks teadmisi protsessi tegeliku toimise kohta. Neist meetodist üks enim uuritud on automaatne äriprotsesside avastamine. Sündmuste logi võetakse kui sisend automaatse äriprotsesside avastamise meetodi poolt ning väljundina toodetakse äriprotsessi mudel, mis kujutab logis talletatud sündmuste kontrollvoogu. Viimase kahe kümnendi jooksul on väljapakutud mitmeidki automaatseid äriprotsessi avastamise meetodeid balansseerides erinevalt toodetavate mudelite skaleeruvuse, täpsuse ning keerukuse vahel. Siiani on automaatsed äriprotsesside avastamise meetodid testitud ad-hoc kombel, kus erinevad autorid kasutavad erinevaid andmestike, seadistusi, hindamismeetrikuid ning alustõdesid, mis viib tihti võrdlematute tulemusteni ning mõnikord ka mittetaastoodetavate tulemusteni suletud andmestike kasutamise tõttu. Eelpool toodu mõistes sooritatakse antud magistritöö raames süstemaatiline kirjanduse ülevaade automaatsete äriprotsesside avastamise meetoditest ja ka süstemaatiline hindav võrdlus üle nelja kvaliteedimeetriku olemasolevate automaatsete äriprotsesside avastamise meetodite kohta koostöös domeeniekspertidega ning kasutades reaalset logi rahvusvahelisest tarkvara firmast. Kirjanduse ülevaate ning hindamise tulemused tõstavad esile puudujääke ning seni uurimata kompromisse mudelite loomiseks nelja kvaliteedimeetriku kontekstis. Antud magistritöö tulemused võimaldavad teaduritel parandada puudujäägid meetodites. Samuti vastatakse küsimusele automaatsete äriprotsesside avastamise meetodite kasutamise kohta väljaspool akadeemilist maailma.Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual performance of these processes.One of the most widely studied process mining operations is automated process discovery.An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log.Several automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models.So far, automated process discovery methods have been evaluated in an ad hoc manner, with different authors employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of non-publicly available datasets.In this setting, this thesis provides a systematic review of automated process discovery methods and a systematic comparative evaluation of existing implementations of these methods with domain experts by using a real-life event log extracted from a international software engineering company and four quality metrics.The review and evaluation results highlight gaps and unexplored tradeoffs in the field in the context of four business process model quality metrics.The results of this master thesis allows researchers to improve the lacks in the automated process discovery methods and also answers question about the usability of process discovery techniques in industry